The Hardball Times -- Max Marchi

In the old days, the game was more exciting

Max Marchi — 2012-01-27T07:12:15+00:00

There was no paraphernalia in the old days with which one could protect himself. No mitts; no, not even gloves, and masks, why you would have been laughed off the diamond had you worn one behind the bat.
- Jim O'Rourke, 1913

I don't think the major league baseball players of today can be compared to the old-timers. I think the slider is a nickel curve and I detest hearing the modern sissies moan about how it has ruined batting averages.
- Frankie Frisch, 1962

I think probably after my generation, the game is going to change. My generation is the last of the old school.
- Darren Daulton, 1997

Former players will always tell you that nowadays the game is easier, while back in their day the pay was low, the fields were uneven, and you had to be a very tough guy to get and keep a job in baseball.

Thus, while I never read about anyone saying the line used for the title, it's quite possible that if you ask a bunch of old-timers, you'll discover that they did used to play a more exciting game, and that today's millionaires take part in rather dull contests.

During the last offseason I introduced a method to rank games by their "excitement factor." The relevant articles that lay down the foundation for the algorithm are What makes an exciting game, revisited and More than three decades of exciting games.

However imperfect a measure of something so subjective will always be, the method was shown to do a pretty good job. When teams traded the lead and the outcome was in discussion until the very end, the games were ranked high. Conversely, whenever one team run out for good with the lead very early, the contest was listed at the bottom. As a refresher, you may want to look at the articles dealing with Division Series, League Championship Series and World Series (World Series at its best and Fall Classics countdown).

One reader e-mailed me with an interesting question. Looking at the top postseason games list, he had the impression that the highest ranks were dominated by recent games. Thus he asked whether that was just an artifact due to the increased number of postseason games. Or are we witnessing an increase in spectacular playoff contests?

Retrosheet has play-by-play data for every postseason game in major league history. I applied my algorithm to the games and grouped them by decades.
The chart below shows the average scores by decade.

Unfortunately it's hard (if not impossible) to interpret what a change of 0.10 in the excitement factor means, as the final number is obtained through a series of statistical transformations. The best I can do to help interpret these difference is to outline a couple of games for comparison.

Game One of the 1927 World Series, a 5-4 Yankees victory over the Pirates, scores very close to 0.1 (the typical postseason game of the '20s), while Game Five of the 1967 World Series, won by the Red Sox 3-1 over the Cardinals, is around -0.1, much in line with the average 1960s postseason game.

If you look both at the line score and the win probability chart of those games, you'll have a hard time telling which must have been the more exciting. Going through the 1927 play-by-play, we see the Pirates threatened in the bottom of the eighth, cutting the Yankees' lead to one run and leaving the tying runner 90 feet from home.
On the other hand the 1967 Cardinals never figured out Jim Lonborg: They connected for just three hits and never were in contention despite the close final score (Roger Maris belted a homer to right with two outs in the ninth for the lone Cardinals run).

However, back to the question. Have postseason games gotten more exciting lately? The chart seems to say that the games got worse from the '20s to the '60s, then bounced back to the original standard.

There's a peak in the '90s. If we think about that decade, a lot of great games come to mind:
{exp:list_maker}Joe Carter's game winner in 1993
Sid Bream coming home in 1991
The entire 1991 World Series
The Marlins winning in extra innings in Game Seven in 1997
and many more{/exp:list_maker}
I don't feel we can come out with anything conclusive from this analysis. From the beginning of the 20th century to the end of the 1960s the postseason was just the World Series, with a maximum of seven games played in a given year. Thus just 50-60 games contribute to the average scores until the '60s, compared to 140 in the '70s, 176 in the '80s, 228 in the '90s and 322 in the first decade of the new millennium.

Luckily, we have a lot more games to work with. In fact Retrosheet offers play-by-play data for regular season contests going back to 1948, thus giving us the opportunity to compare thousands of games each year.

The question can be reformulated: Have the games gotten better in the last 60 years?

Look at the chart below, showing the average score by year.

Even without the superposed smooth line, it appears the answer is a resounding no. The games seem to have steadily been getting more boring since the '70s.

Okay, that's quite a bold statement, as the golden year of 1966 scores 0.04 on average while in the dark times of 2001 the average game scored -0.02. I challenge the readers to choose the better game between the May 30, 1966 one featuring the Orioles at Minnesota (Baltimore won 5-1) and the Sept. 9, 2001 contest in which the Blue Jays visited the Tigers (and won 6-3).

Again, if you look closely, the 1966 game is locked for the first half, while the 2001 Jays take an early lead they never relinquish, and this can make for the difference (0.04 versus -0.02) in the excitement factor.

Sure, the difference separating the best and worst years is very thin. However a trend is there: Starting from the 1970s, the line has steadily gone downward. Is it possible to find a cause for this?

Everyone knows that 1969 is the year of a four-team expansion. It's also the year when divisions were born and the mound was lowered. Any of the three, or their combination, could be the culprit. However, if it was a single change in the game (or a combination of events happening together), I would expect the line to have an initial steep decline, then become flat. If it were expansion, for example, we should see a step down in 1969, then other steps when the major leagues expanded again in 1977, 1993 and 1998.

You might remember that the game excitement score is the synthesis of three factors, one for the importance of the final part of games, one for rallies and one for equilibrium.

The next charts depicts the trend for the three factors.

The rally factor seems to be the force driving down the game excitement. It seems that coming back has consistently become more difficult over the years.

Does this make sense? I think so.

I would indicate relief pitching as the explanation for that. Baseball has gradually moved from having the starting pitcher going the full nine innings to the current habit of having multiple relievers come out of the bullpen in a single game and, perhaps, in a single inning.

Coming back has to be harder when there's never a tired arm on the mound, the superstar left-handed batter has to face a southpaw specialist brought in just for him, and setup-closer combinations like Jonny Venters and Craig Kimbrel can make games just seven- inning affairs.

Will the trend continue? Are we doomed to watch fewer and fewer thrilling contests in the future?

If the relievers usage hypothesis is sound, it's hard to imagine an increase of specialization from where we stand right now, unless teams completely abandon the concept of starting rotation and select their pitchers inning by inning.

Thus, we should not get worse than this. Let's hope the evolutions that sooner or later will happen in baseball can make up for what we have lost during the past decades.

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The 2011 Yogi Berra Award

Max Marchi — 2011-11-23T09:31:15+00:00

Awards are presented in November, and for the third time, The Hardball Times is going to name the Yogi Berra Award winner.

The great Yankee catcher used to swing at everything, and when asked about his habit of swinging at bad pitches, he once replied with one of his trademark quotes: "If I can hit it, it's a good pitch."

A young baseball fan, reading about Yogi's tendency to go after every pitch, might be tempted to assume he struck out a lot. Nothing is further from reality, as you can see from this FanGraphs chart. (Do not mind the final data point, as it is based on nine plate appearances.)

Thus, in crowning the third recipient of the Yogi Berra Award, we are looking for hackers who get good results from their aggressive approach at the plate.

You can read about the previous awards by checking out the 2009 and 2010 articles. In both occasions Pablo Sandoval was named as the winner.

Since bad-ball swingers are the subject of this article, a definition of bad-ball is needed. As was done in the past editions, pitches that are called strikes fewer than 10 percent of the time (based on where they crossed the plate—thanks to Sportvision and MLBAM for PITCHf/x data) are considered bad balls. Only players who have been fed at least 300 such pitches are considered for the analysis.

Who are the free-swingers? Below are listed the 10 players with the highest percentage of swings at bad pitches.

Player               Pct    Pitches
Alfonso Soriano       41        871
Humberto Quintero     39        405
Vladimir Guerrero     36       1003
Alex Gonzalez         36        901
Miguel Olivo          36        926
Mike Carp             36        354
Reid Brignac          36        418
Pablo Sandoval        36        718
Mark Trumbo           35       1028
Rod Barajas           34        652

However, as was said above, a good candidate for the Yogi Berra Award must be able to connect when swinging at bad pitches. Thus, below are the players with the lowest whiff percentage on bad balls.

Player          Whiff%   Swings
Juan Pierre         11      217
Marco Scutaro       11       44
Todd Helton         17      119
Brett Gardner       19      145
Angel Pagan         20      176
Eduardo Nunez       20       84
Ichiro Suzuki       21      331
Brian Roberts       21       94
Jamey Carroll       21      170
Ryan Sweeney        22      120

Obviously, Marco Scutaro can not hope to be included in the race for the award, as he is good at connecting when he decides to go for a bad pitches but chooses to do so very infrequently. On the contrary, Juan Pierre and Ichiro Suzuki, with 217 and 331 attempts, respectively, are good candidates.

Suzuki and Pierre are also at the top of the following list. These are the players with the highest number of base hits obtained on bad pitches (BPH).

Player            BPH
Ichiro Suzuki     102
Juan Pierre        95
Brandon Phillips   82
Martin Prado       81
Gaby Sanchez       78
Pablo Sandoval     75
Adrian Gonzalez    74
Matt Wieters       71
Mark Ellis         68
Vladimir Guerrero  65

We don't have a clear-cut winner for this edition of the award, since the players with a combination of hacking attitude and low swing-and-miss ratio did not excel with the bat in 2011. (Both Pierre and Suzuki were close to replacement, according to FanGraphs WAR stats.) Lacking a better option, Juan Pierre gets our nod due to his ridiculous percentage of whiffs—11 percent, the same obtained by the very disciplined Marco Scutaro.

Getting historical

What about the great hackers of the past, including Yogi himself? There's no PITCHf/x data going back to the late '40s, when Berra started his career, but a handful of stats can be combined to outline the hardly-whiffing free-swingers of the past.

The AVG/OBP ratio (with intentional walks removed when data are available) can be used as a proxy of the batters' reluctance to let pitches go past. The K% (strikeouts divided by PAs) tells us how much the free swingers came out empty after their efforts.

The following hypothetical list of Yogi Berra Award recipients throughout baseball history (since integration) has been obtained by naively combining the two stats (adding the AVG/OBP ratio to 1-K%). Batters with fewer than 300 PA in the season and batting below replacement have been removed.

Season   Player
1947     Dale Mitchell
1948     Alvin Dark
1949     Ted Kluszewski
1950     Ted Kluszewski
1951     Nellie Fox
1952     Red Schoendienst
1953     Don Mueller
1954     Don Mueller
1955     Nellie Fox
1956     Vic Power
1957     Red Schoendienst
1958     Vic Power
1959     Bobby Richardson
1960     Russ Nixon
1961     Roberto Clemente
1962     Vic Power
1963     Frank Malzone
1964     Willie Smith
1965     Jesus Alou
1966     Felipe Alou
1967     Jesus Alou
1968     Felix Millan
1969     Al Oliver
1970     Jesus Alou
1971     Manny Sanguillen
1972     Bill Buckner
1973     Manny Mota
1974     Bill Buckner
1975     Larry Bowa
1976     Bill Buckner
1977     Bob Bailor
1978     Bill Buckner
1979     Lou Piniella
1980     Bill Buckner
1981     Bill Buckner
1982     Bill Buckner
1983     Mickey Hatcher
1984     Don Mattingly
1985     Bill Buckner
1986     Don Mattingly
1987     Don Mattingly
1988     Don Mattingly
1989     Brian Harper
1990     Tony Gwynn
1991     Brian Harper
1992     Brian Harper
1993     Tony Gwynn
1994     Carlos Baerga
1995     Tony Gwynn
1996     Lance Johnson
1997     Tony Gwynn
1998     Tony Gwynn
1999     Tony Gwynn
2000     Darrin Fletcher
2001     Ichiro Suzuki
2002     Randall Simon
2003     A.J. Pierzynski
2004     Ichiro Suzuki
2005     Placido Polanco
2006     Paul Lo Duca
2007     Placido Polanco
2008     Cristian Guzman

So Yogi himself is not on the list, but he is in the top five each year from 1947 to 1950 (with two second-place finishes) and finished sixth in 1951.

Finally, to have a sort of hackers hall of fame, let's assign 10 points to the top free-swinger in each season as selected with the method above, nine to the runner-up, and so on, down to one point to No. 10.

	Player	        Points
1	Tony Gwynn	   144
2	Bill Buckner	    89
3	Al Oliver	    80
4	Ichiro Suzuki	    77
5	Don Mattingly	    67
6	Willie Davis	    56
7	Nellie Fox	    52
8	Brian Harper	    48
9	Vic Power	    47
10	Placido Polanco	    45
11	Dale Mitchell	    42
11	Vlad Guerrero	    42
13	Matty Alou	    41
14	R. Schoendienst	    40
14	Smoky Burgess	    40
14	Ted Kluszewski	    40
17	Garret Anderson	    39
17	M. Sanguillen       39
17	N. Garciaparra      39
20	Lance Johnson       37
20	Mickey Rivers       37
20	Yogi Berra	    37

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Baseball at 2:00 a.m.: The postseason from the other side of the pond

Max Marchi — 2011-10-13T10:02:15+00:00

{exp:list_maker}1. Wake up at 1:00 a.m.
2. Watch the game.
3. Have breakfast around the sixth or seventh inning.
4. If the game ends quickly, take a nap.
5. Go to work and try to look alert.
6. Back home, have dinner.
7. Go to bed early.
8. Repeat until the end of the World Series. {/exp:list_maker}
That's what it takes to watch the postseason for someone living in Europe, six hours ahead of Eastern Standard Time.

Actually it hasn't always been that easy.

The '70s: U.S. bases radio

Note: I was born at the very end of the decade, so this paragraph is more retelling than personal experience.

There was no way in Italy to watch MLB back in the '70s, but there were baseball nuts who just kept turning their radio toggle until they found some station broadcasting the World Series for the benefit of U.S. soldiers serving in European bases. I personally know one guy who became a Red Sox fan after following the 1975 Fall Classic this way—and he didn't even speak English!

The '80s: VHS making the rounds

Video cassette players and recorders began to appear in Italian houses in the '80s. Thus, the potential for a new and enhanced way to follow Major League Baseball was there. But you needed to find someone who actually possessed tapes of the World Series. I was kind of lucky. One of the best national baseball teams plays in my city, and back then it usually employed a couple of U.S.-born players as imports, and the imports had the tapes!

I was not too far behind in the queue for getting the cassettes as I played second base in the under-15-years-old team, and the U.S. players occasionally came to our practice to teach us a some baseball fundamentals. Trouble was, the first kid who got the tapes (on a promise of creating duplicates, because he owned two recorders) was the one taking forever watching the games. And when he finally passed along the yearned-for treasure, there was no trace of the promised duplicates.

It wasn't that bad—I believe I even watched one World Series before the ensuing season began! Well, maybe memory is failing here, probably it was before the ensuing season ended.

The '90s: enter Pay-TV

The 90s brought Pay-TV to Italy. In the beginning there were just two pay channels, one for movies and one for sports. You didn't even need a dish to receive them. Also, without the decoder you would get a mute version of the broadcast with negative colors.

I perfectly remember a light-skinned Otis Nixon, wearing a black Braves uniform, laying down a two-out bunt with a runner on third and running on the green base paths. He did not reach the black first base bag in time, and the Yellow Jays celebrated their first World Championship.

I never got Pay-TV. In the beginning, I couldn't get over the fact that I had to pay to watch TV. (Moreover, I wasn't earning any money back then as I was in high school.) Then things became more complicated: You needed the dish, for one. Then each year it wasn't clear whether the sport channels would broadcast MLB (the only reason for which I would remotely start thinking about paying to watch TV).

So the '90s, like the '80s, were a decade of VHS for me. And since the number of people subscribing to Pay-TV was growing, I was able to get the tapes first-hand, a big improvement over the wait-for-the-kid-who-promised-the-duplicates times. In the '90s, I watched the games no later than a week after they were played, and very often, a few hours after they had been completed.

There was a little problem in the 90s: Tape measure. No, I'm not talking about moon shots by the biggest sluggers. The videocassettes had a capacity of four hours at most, and with the extended postseason commercial breaks, sometimes the game would not fit onto the tape. That did not occur every time, just in game going to extra innings.

Can anybody tell me what happened when Luis Gonzalez went to the plate to face Mariano Rivera? And what about Charles Nagy versus Edgar Renteria?

Third millennium: MLB.tv

I got my first MLB.tv subscription just in time to witness the Red Sox sweep the Cardinals and end their eight decades long World Series drought. MLB.tv was the deal I was looking for: Pay to watching baseball and nothing else. (Pay-tv offered a limited number of games plus a lot of other stuff, not baseball or sports related, I didn't care for.)

Thus, for the last few years, I've been able to watch the postseason live (and the regular season's day games, too).
And that's why you don't get any article from Max Marchi in October (look back at the eight points listed at the beginning of this article, which I wrote back in June).

Why not the following day?

You might ask why can't I watch the game the following day after work. Well, I tried that in the past. Theoretically, I would just need to stay away from the internet and e-mail to avoid knowing the result before watching the game. Though that's increasingly challenging in the third millennium, it can be done.

TV is not a problem. There is so little interest in baseball here in Italy that the chances you hear the World Series result on air are slim to none. (Exception: When the Red Sox broke The Curse, it made the news). For the same reason, you'll hardly run into a baseball nut who can't wait to tell you about the game. And the few baseball nuts, as soon as they meet you, they first ask you if you have already watched the game, to avoid playing the spoiler.

So, why not? Because every time you plan to watch the game the following day after work, you run into somebody (either at work or during the commuting), who couldn't care less about baseball. He will tell you the outcome of the game. He is not armed with bad intentions; on the contrary, he wants to please you, letting you know he knows about that strange game you like.

Because of that, it's been three years since the last time I tried to watch games on a 12-hours delay. And because of that, as you read this, my boss is trying to figure why I look like a zombie.

Poll

What's the most annoying character / event in this story?
{exp:list_maker}a. The kid not doing the duplicates
b. Running out of tape on the deciding at-bat
c. The guy who doesn't give a damn about baseball for 364 days a year telling you the outcome of the Series. {/exp:list_maker}

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Who is playing the percentages? What are the percentages?

Max Marchi — 2011-10-11T10:33:15+00:00

- You! Strawberry! Good effort today. Take a lap and hit the showers. I'm putting in a right-handed batter.

- Pinch-hitting for me?

- Yes. You're a left hander, and so is the pitcher. If I send up a right-handed batter, it's called playing the percentages. It's what smart managers do to win ball games.

- I've got nine home runs.

- You should be very proud. Sit down. Simpson! You're batting for Strawberry.

(Mr. Montgomery Burns and Darryl Strawberry, manager and right fielder, respectively, of the Springfield Nuclear Power Plant softball team.)

This article was inspired by this thread at The Book Blog, in which Mitchell Lichtman criticizes Tony La Russa's managerial choices—well, actually "criticizes" is quite an euphemism.

The question is: Your starter has breezed through eight innings and is due to bat. Your team is leading by one run. Do you pinch hit for him?

Tony La Russa did not. It was the do-or-die fifth game of the National League Division Series, Chris Carpenter had blanked the Phillies lineup and was going to lead off the bottom of the eighth.

According to Lichtman, a.k.a. MGL, going to a better hitter was a no-brainer according to the numbers. You can read his arguments and his own words in the original thread, but the following summary should be a reasonable approximation of his position.

You pinch hit because:
1. You get a better chance of producing in your offensive inning.
2. Since every pitcher gets worse as he goes through the opposing lineup time after time, a fresh closer is a better option than a starter going to face the same batters for the fourth time.

Here we'll expand a bit on point number two. Full disclosure: If I were managing in a deciding game and that situation occurred, I would NOT substitute for my starter.

Baseball talent

Let's suppose we can visualize the distribution of baseball talent among people. It would probably be something like this.

The guys who actually play baseball should all be on the right end of the curve, with the major leaguers being on the extreme right part of the chart. Let's zoom in on that part of the chart and focus on pitching talent. Something like the following might be reasonable.

No deep analysis has been performed to place those names on the talent spectrum, thus the positions are absolutely debatable. However, let's suppose they're placed appropriately.

We have Justin Verlander and CC Sabathia as the elite pitchers; Carpenter as a good-to-great player (if we were to consider the 2005/2006 seasons, his name would have been more to the right). Ted Lilly, who according to FanGraphs is one win over replacement level, can be considered a legitimate major leaguer. Finally, Dontrelle Willis, who has been attempting comebacks year after year, has to stay in the baseball limbo where the so-called Quad-A players have to live.

Starter versus closer

Let's now try to visualize Carpenter's effectiveness as the game progresses and compare it with Jason Motte's effectiveness. Again, the placement of labels on the chart is complete (though a bit educated) guesswork on my part and might not reflect the real values.

Two questions:
1. Why is Motte's name in the above chart marked with an asterisk?
2. And why is Carpenter's effectiveness the fourth time through the lineup marked with the "§" sign?

If you compare Carpenter's and Motte's ERAs (3.45 and 2.25 in 2011, respectively), or their batting averages allowed, or other more or less advanced metrics, you might be tempted to say Motte is the better pitcher, but we all know that's not the case.

Motte enters the game in the ninth, so he does not face batters a second, third and fourth time through the game, when he is tired and the opponents have had the opportunity to time his pitches. Also, you have to consider that while Motte can put everything he has on every pitch, Carpenter has to pace himself if he wants to throw multiple innings.

Thus, the asterisk means: Yeah, Motte's average effectiveness (that is, his ability to prevent opponents from scoring runs) looks better than Carpenter's average effectiveness, but that's because he pitches in a different setting. So, in the second chart, Motte's name would be to the left of Carpenter's name, not to its right.

Also, if you look at OPS allowed, you would think Carpenter has some kind of resurgence late in the game.

situation			 OPS
First time through the lineup	.676
Second time through the lineup	.745
Third time through the lineup	.732
Fourth time through the lineup	.682

Several pitchers show those kind of numbers. Does it mean that pitchers get some kind of energy injection when they see the finish line? No, you know better.

Starters are allowed to pitch deep into games on nights when they are performing well, but get the quick hook when they show "they don't have it." If you forced managers to leave pitchers on the mound until they have completed their fourth time through the lineup no matter of the score, you would not see those "resurgences."

(One can look at old-timers' numbers—back when pitchers were supposed to complete their games—to prove this. Don Newcombe is a good example (Baseball-Reference time-through-the-order stats). I have done a cursory look and found similar patterns for Bob Feller, Robin Roberts and Whitey Ford.)

Thus, the "§" means: We have taken care of the selection bias issue.

Summarizing this section: If we suppose I have correctly laid down the labels in the chart, it's better to have a fresh Motte out to the bullpen than having Carpenter facing the opposing team for the fourth time.

Good and bad days

The sentence closing the previous section is true on average, or if the players are robots always performing at the same level (same performance, same decline each time through the order, and so on).

But players, fortunately, are human beings—if you had some question about Verlander not being human, the postseason games played so far should have convinced you of the contrary—and human beings have good and bad days.

The chart above shows Carpenter's Game Scores throughout his career. Though we should expect Game Scores variation for robots as well (due to luck), it's safe to assume a significant portion of the variation in the chart is caused by Carpenter having good and bad days (due to health issues, psychological factors, luck...whatever).

Even in 2005 (shaded on the chart), his Cy Young season, Carpenter had a couple of extremely bad outings. It's very possible that on those occasions he threw as well as in any other start and simply had bad luck, but it's quite likely that he was not 100 percent: he might have not slept well the previous night, some minor ailment could have been affecting him, or he simply "didn't have it" that night.

The chart below should not be too unreasonable.

On his best days, Carpenter can be the best pitcher in the game, while on an awful night he will resemble a back-of-the-rotation pitcher.

What kind of Carpenter was on the mound in the NLDS Game Five?

That question is why teams can not be run by computers.

The average Motte facing the opposing lineup for the first time in the game is better than the average Carpenter facing that lineup for the fourth time.

I'm pretty sure La Russa knows this; otherwise, he would not have relievers in his bullpen. If La Russa decides to leave his ace on the mound for the ninth inning, it's because he believes Carpenter is having one of his best days. Thus, the chart in La Russa's mind should look like the following.

Note: Carpenter's first and second time through the lineup, in this scenario, are literally off the charts.

Carpenter on his best night is probably better the fourth time through the lineup than the average Motte coming out of the pen. Yeah, Motte might also be having the best night of his life, but there's a difference.

For Carpenter we have eight innings of blanking the mighty Phillies. Sure, it can be the usual Carpenter with a lot of luck on his side, but with eight goose eggs against a powerful lineup we are entitled to shift (if ever slightly) our a priori idea of Carpenter's effectiveness for the night. With Motte we don't have any clue, except what he and the bullpen catcher can tell us.

La Russa bets Carpenter is blessed with an inordinately great condition and that Motte is his usual self. Is that bet ill-advised?

Okay, time for some numbers.

I looked at games played in the past 20 years, thanks to the invaluable Retrosheet data. I selected all the instances in which the starting pitcher has completed eight innings giving up one run at most. These should be the circumstances when the manager can believe his starter "has it" and can complete the game.

I removed the games in which the offense had provided the pitcher more than three runs. Thus, we are dealing with situations in which the game is still on the line, and the manager should be trying to maximize his chances. (In a blowout the skipper's choices could be dictated by having to rest the bullpen or wanting to try a young arm.)

The games were then split in two groups: Games with the starter beginning the ninth (STARTER) and games with a reliever beginning the ninth (CLOSER).

Here's how the two groups fared, with more than 1,000 games represented in each group.

runs		percentage
allowed      CLOSER  STARTER
  0  		76 	74
  1  		14 	16
  2   		 7  	 5
  3   		 2  	 3
  4+   		 0  	 1

Looking at the numbers above, the decision on whether leaving the starter in or removing him appears as a coin flip. However, the above table can suffer from selection bias, with three possible sources of bias coming to my mind.

It's not a given that the quality of opposing lineups does not influence the choice of going (or not going) to the bullpen. I believe the quality of offense faced is equal between the two groups, but a check should be done.

On the contrary, I'm pretty sure that the talent of both the closer and of the starter play a role in the decision. If you have Mariano Rivera, you are more likely to give him the ball even when the starter has thrown eight frames of shutout ball, which has the effect of deflating the CLOSER numbers in the table above. But if the starter is a top-notch player (and this is true in many of the games we are analyzing) and the bullpen is not dependable, the manager will lean toward the slow hook, which should deflate the numbers in the STARTER column above.

So what are the percentages?

I would say you could flip a coin and make your decision. Tom Tango, performing different analyses, has arrived at a similar conclusion. And this is noteworthy. Many of us (I, for one) would have believed that leaving the starter is the only choice. Instead, calling in the closer is an equally sensible choice. And when you factor in the pitcher being due to the plate in the National League, it becomes even more sensible.

Giving Lichtman's post the title Worst managing ever surely attracts some extra clicks, but it also overstates reality, even when you add to the mix the highly-questionable bunt calls not analyzed in this article.

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Extra plays made: Should we change our point of view?

Max Marchi — 2011-08-05T08:32:15+00:00

Psst... Let me tell you this: You can't compare fielders' ratings with any of the existing defensive metrics.

How do you build a defensive metric?

Take a player, find out how many plays an average player would have made given the same chances he had, subtract those (expected) plays from the (actual) plays made by the guy you are evaluating, and you are done.

To my knowledge that's ow the most known fielding metrics are built.

{exp:list_maker}Mitchell Lichtman's UZR: check.
Sean Smith's TZR: check.
Dan Fox's SFR: check.
David Pinto's PMR: check.
The Fielding Bible plus/minus: check. {/exp:list_maker}

(Note: Actually, most of the systems perform the extra step of converting plays made into runs saved.)

It seems a consensus has been reached on how a fielding evaluation system should look. Thus, we just have to wait for the data to get better and better for the defensive ratings to get more and more reliable.

The data are getting better. Baseball Info Solutions, with its crew of video scouts, yearly improves its database either by introducing more rigorous quality checks, or by adding new information, such as hang time, fielders' positioning, and so on. Meanwhile, Sportvision is trying to capture the position of the ball and the players at any moment during every ballgame, with its multiple camera system.

When presented with a sample of data from Sportvision FIELDf/x, Greg Rybarczyk immediately proposed a defensive metric based on those data. You can read about his True Defensive Range in The Hardball Times Baseball Annual 2011, in the article he co-authored with Kate McSurley (An introduction to FIELDf/x). However he simply applied the basic steps outlined at the beginning of this article to what appears to be a very detailed and accurate database.

Indirect vs. direct standardization

Epidemiologists had been using the indirect standardization method for a long time when advanced baseball fielding metrics began making their appearances. Indirect standardization is a way of taking into account the characteristics of a population (its age structure, for example) when looking at the frequency of an event (let's say the mortality due to lung cancer).

It works like this: You take the mortality rates of a standard population (say the entire nation) and you apply the age structure of the population under scrutiny (say a county); this way you get the expected number of deaths, to be compared to the number of deaths that actually occurred.

Sounds familiar? Yeah, it's the same thing every major fielding metric does, except epidemiologists prefer dividing observed events by expected events, instead of subtracting them, as baseball analysts do.

Epidemiologists also use an alternate method: direct standardization.
Applying direct standardization to fielding evaluations would consist of the following steps: Take the distribution of chances the average player faces, calculate the expected plays a given player would make given those chances, subtract the expected plays from the actual plays.

What's the difference between the two methods?

Indirect standardization (or what baseball analysts are currently using) assigns the same chances faced by the player under evaluation to the average fielder in order to obtain the expected plays. If we are measuring the defensive prowess of a shortstop who faced 150 grounders to his left, 250 straight at him, and 100 to his right, we would calculate the expected plays by assigning 150 grounders to the left of the average shortstop, 250 straight at him and 100 to his right.

Direct standardization assigns the same distribution of chances faced by the average player to the player under evaluation. Let's say the average shortstop has to deal with 200 grounders hit straight at him, 150 to his left and 150 to his right. Thus we would calculate the expected plays for our shortstop by assigning him 40 percent of balls straight at him and 30 percent both to his left and to his right.

Why have baseball analysts chosen the indirect standardization way en masse?

Pros and cons of the two methods

When you calculate the expected plays in the direct standardization method, you base your result on a very limited sample. In fact, in our example of a metric which simply divides the opportunities in three buckets (left, straight, right), we would use the percentage of balls the shortstop under scrutiny turns into outs for each bucket; thus the expected plays are based on a single player sample.

On the other hand, indirect standardization uses information from every shortstop when calculating the expected plays. Epidemiology textbooks suggest to use the indirect method for small populations, when the stratum-specific rates* are unreliable. (* In this case the stratum-specific rates would translate in out-conversion rates to the player's right, left and center)
Thus we have gone the right way.

Except, epidemiology textbooks give the following warning: When you use the indirect method, you can compare a population to the standard population, but you can't compare two populations between them. Translate it to baseball defense: You can compare a shortstop with the average shortstop, but you can't compare two shortstops between them.

Wait! Am I saying that a plus-15 shortstop (a shortstop with 15 more plays made than expected) has not necessarily performed better than a plus-10 shortstop, even if they had exactly the same amount of opportunities? (Please note the bold above: it's "performed better" rather than "is a better defensive player," because we don't want to enter the perilous terrain of trying to guess true talent on the basis of performance.)

An example

Let's pretend we know exactly the true talent of two shortstops. Player A converts 90 percent of balls hit straight at him into outs, and 37 percent of both balls to his right and to his left. Player B's rates are 85 percent to the middle and 36 percent to both sides.
The average shortstop's are 80-35-35. Thus Player A is superior to Player B on every batted ball and both are above average.

Let's also assume they perform exactly according to their respective skills.

Player A faces 100 grounders straight at him, 100 to his right and 400 to his left. Do the math and you get 275 plays for him versus 255 for the average shortstop, a net of plus-20 plays.

Player B faces 400 grounders straight at him, 100 to his right and 100 to his left. With the necessary multiplications you get 412 successful plays for him, 390 for the average shortstop—a plus-22 for Player B.

Despite A being superior to B and both having faced 600 total chances, and having performed according to their skills, Player B is rated higher.
The only reason for this outcome is the different set of opportunities, something beyond the players' control.

Okay, I can hear you say: "If the pitchers playing with A allow an inordinate amount of balls to his left, he should position himself accordingly—that's part of his defensive duties as well!" Right. But you could substitute the left/middle/right buckets with something like hard/regular/soft hit or whatever you want (you can even play God and say easy/medium/difficult).

So, let me reiterate the issue. When using indirect standardization (i.e., when using whatever existing fielding metric), you are entitled to say that both Player A (+20 plays) and Player B (+22) performed better than the average shortstop, but there is no way you can infer Player B performed two plays better than Player A. (In fact, we saw that Player A actually performed better than Player B).

What would happen with direct standardization?

The average shortstop, facing 600 balls evenly distributed among center, right and left, would record 300 successful plays. Player A, given the same distribution of 600 chances, would convert 328 of them, or plus-28 over the average shortstop. Player B, again with an equal set of opportunities, would record 314 outs, or plus-14.

With the direct standardization, the real ranking emerges.

Should we make the switch?

We are in a conundrum. If we move to direct standardization, we need a reliable estimate of a single player's success rate on, for example, balls hit softly at an angle of 10-15 degrees. Chances are, for some buckets you have to rely on as much as one or two chances—even no chances at all.

Actually this issue would be somewhat mitigated by smoothing techniques. In fact, the success rate of the above example bucket is surely correlated with the success rate on balls softly hit at either an angle of 5-10 degrees or 15-20 degrees, and also with the success rate on balls hit at an angle of 10-15 degrees with medium force. Nearly every opportunity faced by a player can contribute useful information for each considered bucket—with decreasing weight as the opportunities become more and more different. (Ideally we treat data as continuous, rather than artificially split opportunities into buckets)

However, maintaining the indirect standardization method exposes us to the risk of improperly ranking players, as I have shown with an example.

You may have noticed that in order to get the paradoxical result, the players in the example face two completely different distributions of chances. When the distributions are similar (as should be the case for fielding chances) the rankings resulting from an indirect standardization would not be too far from reality. But, if the players face similar distributions of opportunities no standardization is needed; i.e. the success/opportunity ratio is sufficient, and the labor of classifying batted balls by angle, velocity and so on, is unnecessary.

I believe fielding metrics should shift to the direct standardization method when data become more objective, detailed and unbiased. Until then the indirect standardization is an improvement over no standardization at all when players face different set of opportunities (but that's when improper ranking might come out).

Thus, when you look at fielding leader boards, keep the following in mind. If a player has a positive rating he has performed better than average; if a player has a negative rating he has performed worse than average. But there's no way you can tell who has performed better between two better-than-average (or worse-than-average) players.

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Top games of the week: July 17-23

Max Marchi — 2011-07-24T08:09:15+00:00

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Top games of the week: July 10-16

Max Marchi — 2011-07-17T08:12:15+00:00

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Top games of the week: July 3 to 9

Max Marchi — 2011-07-10T14:08:15+00:00

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Evaluating catchers: Framing pitches - part 3

Max Marchi — 2011-07-08T09:04:15+00:00

In the past couple of articles catchers' framing has been analyzed here: (part one - part two).

Before diving into part three, let me explain what I considered framing in those articles (and in this one, as well). I should have made this premise clear at the beginning of the first article of this series, but I neglected to do that, probably believing that the definition would emerge from the text. Some of the comments made me realize that was not the case, so here I'm trying to redeem myself.

What has been considered framing in this series?

The way a catcher receives a pitch can slightly change the likelihood that said pitch is declared a strike by the umpire. I suspect there are several ways to alter the odds, and sometimes even the catcher himself is not aware he is doing something on that effect.

So, when talking about framing in the past couple of articles, I did not have any specific action in my mind: Bringing the glove inside the zone (that, in my opinion, would indeed reduce the chances of a called strike), positioning the body so that the pitch arrives right to a motionless glove, catching the slider closer to the plate so that the breaking action had not brought the ball too far away from the plate, and so on.

What I had in mind is the following: A catcher receives 100 pitches. Given their characteristics (pitch type, count, location, pitcher throwing it, batter facing it, umpire judging it), 60 of them should have been called for strikes. If 75 of them are actually declared strikes, then the catcher has converted 15 balls into strikes by virtue of his framing skills, no matter how he physically achieved the result.

If you feel that "framing" is a misnomer for what I'm measuring, feel free to suggest something else in the comments section below.

From rate to count

Until part two, the runs saved by framing were calculated by prorating the increase (decrease) in probability of a called strike due to the catcher to an estimated number of borderline pitches.

For the purposes of this article, the catcher effect has been calculated on every single pitch. Also, while the average run value of turning a ball into a strike was previously used, now the count-specific differential is adopted. Finally, framing for high and low pitches delivered to the middle part of the plate has also been thrown into the mix.

Unfortunately there is a portion of the PITCHf/x database that does not get into this analysis yet, as the ultimate statistical model would combine a spatial component with the multilevel structure (this stuff is not available on the stat software I use). The pitches falling in the green area in the diagram shown below are part of the analysis.

Let's go with an example.

According to the model employed, a 2-1 fastball from Roy Halladay to Albert Pujols delivered two inches outside the middle of the plate one foot and eight inches from the ground has a 44 percent chance of being declared a strike if Dana DeMuth is calling the game.
If such a pitch ends up being called for a strike, we assign the catcher (1 - .44) * .181 = .10 runs; otherwise he gets (0 - .44) * .181 = -.08 runs.

The first number inside the parenthesis is either zero (ball) or one (strike). Then you have the predicted probability of a called strikes given all the considered factors (.44 in our example). Finally, you multiply for the run value of converting a ball into a strike on the given count (.181 on a 2-1 count).

Seasonal rankings

-------2008 top ten-------
           player   G   RV
     Brian McCann 138 23.6
   Russell Martin 149 22.5
      Jose Molina  97 20.3
    Yadier Molina 119 19.2
        Paul Bako  96 13.8
      Chris Coste  78 10.8
        Joe Mauer 139 10.6
       David Ross  54  8.0
      Brad Ausmus  77  7.2
 Yorvit Torrealba  67  6.7

------2009 top ten------
         player   G   RV
   Brian McCann 127 17.7
   Ryan Hanigan  88 13.5
    Jose Molina  49 12.7
     Gregg Zaun  83 12.2
 Miguel Montero 111 12.1
     David Ross  52 10.1
 Russell Martin 137  9.9
  Yadier Molina 138  9.3
  Bengie Molina 123  8.4
      Joe Mauer 109  8.1

------2010 top ten-------
          player   G   RV
     Jose Molina  56 16.9
 Jonathan Lucroy  75 16.9
    Brian McCann 136 15.9
   Yadier Molina 135 15.8
  Russell Martin  93 12.0
    Ryan Hanigan  68 11.0
    Matt Wieters 126 10.2
  Miguel Montero  79  9.8
    Geovany Soto 104  9.6
    Buster Posey  76  8.8

Brian McCann tops both the 2008 and 2009 lists and comes up third in 2010. Several other names recur in those short lists: Various Molinas, Russell Martin, Joe Mauer, David Ross, Miguel Montero and Ryan Hanigan.

As you can see, the best catchers are contributing over one win with their framing skill. Jose Molina unbelievably tops the 2010 list despite having played just 56 games. (Remember, we are now dealing with a counting stat!) While a player can put up extreme numbers in a small sample, the fact that Jose recorded similar numbers both in 2008 and 2009, should leave out the possibility that he got lucky and had all the calls come his way in 2010.

Sure, the prorated three to four wins he would produce in 130 games behind the dish would need to be regressed somewhat, but it's hard to believe he does not countribute an extra win per season thanks to his framing ability.

-----2008 bottom ten-----
         player   G    RV
    J.R. Towles  53  -4.7
   Brandon Inge  60  -4.8
    Mike Rabelo  32  -5.6
      Toby Hall  37  -6.0
   Gerald Laird  88  -6.2
  Kenji Johjima 100  -8.3
 Chris Iannetta 100  -9.3
 Dioner Navarro 117 -10.9
   Nick Hundley  59 -11.8
    Ryan Doumit 106 -29.8

-----2009 bottom ten-----
         player   G    RV
    Omir Santos  91  -6.9
     Koyie Hill  79  -8.2
    Mike Napoli  96  -8.4
 Dioner Navarro 113  -8.6
   Jorge Posada 100  -9.3
    Rob Johnson  80  -9.4
    Kurt Suzuki 135 -11.9
  Kenji Johjima  70 -15.9
    Ryan Doumit  71 -16.2
   Gerald Laird 135 -16.2

----2010 bottom ten-----
        player   G    RV
  Nick Hundley  76  -4.4
   John Hester  33  -5.6
     John Jaso  96  -5.8
    Lou Marson  87  -6.8
    Adam Moore  59  -7.4
 Jason Kendall 118  -7.7
  Gerald Laird  87 -10.8
   Ryan Doumit 100 -10.9
   Rob Johnson  61 -11.8
  Jorge Posada  83 -14.5

The bottom lists also display recurring names. The 2008 run values have Pearson's correlation coefficients of .69 and .66 with the 2009 and 2010 values, respectively, while 2009 and 2010 have a coefficient of .82.

Park effects

The proposed model does not take park effects into consideration. Considering the really small sample of catchers that changed teams during the 2008-09 and 2009-10 offseasons, there does not seem to be a substantial variation in their ratings.

----------Rating of players changing uniforms-----------
			     Run Value	 Run Value/130 G
           player   seasons seas1 seas2   seas1    seas2
      Rod Barajas 2009-2010  4.7   3.4      5.1      4.6
   Kelly Shoppach 2009-2010 -2.2  -0.2     -3.5     -0.5
       David Ross 2008-2009  8.0  10.1     19.3     25.2
  Ramon Hernandez 2008-2009 -2.3   0.1     -2.4      0.2
    Bengie Molina 2009-2010  8.4   5.8      8.9      6.7
     Chris Snyder 2009-2010  4.7   8.5     10.9     10.9
  Victor Martinez 2009-2010 -5.7  -1.7     -8.7     -2.0
    Jason Kendall 2009-2010 -3.2  -7.7     -3.1     -8.5
     Miguel Olivo 2009-2010 -2.2   2.4     -2.8      2.8
 Yorvit Torrealba 2009-2010  0.2   4.8      0.4      6.8
       Gregg Zaun 2008-2009  5.4  12.1      8.9     19.0
  Victor Martinez 2008-2009  2.6  -5.7      6.1     -8.7
     Gerald Laird 2008-2009 -6.2 -16.2     -9.2    -15.6
   Ivan Rodriguez 2009-2010 -2.2   8.6     -2.5     11.0

Note: Catchers above are listed from lowest to highest difference in Run Value. Fourteen catchers who changed uniforms and played more than 50 games in consecutive seasons sport a .67 correlation. For comparison, 53 catchers who didn't move to another ballclub record a .82 correlation.

Also, catchers' performances at home and on the road are highly correlated, sporting a .82 Pearson's correlation coefficient. However, it should be noted that home Run Values are generally higher than away Run Values.

Given all the above, one would be tempted to dismiss the park as an important effect and state that somehow catchers are more adept at putting their framing skills into play at home. However, when digging deeper into this issue, the following cases emerged.

During the full period considered (2008 to May 2011), Brian McCann totaled 47.5 runs at home and 30.7 on the road. Is that all due to the possible familiarity effect mentioned above? During the same time span, the visiting teams recorded a composite +12.2 runs at Turner Field.

Meanwhile, Ryan Doumit has been -22.7 at home and -39.9 on the road, while opposite teams have scored +20.8 runs at PNC Park.

While the bottom line is that McCann is great and Doumit is poor at framing pitches, both have played half of their games in parks that seem to favor strike calls. Thus, adding the park to the model will likely improve the final ratings.

Final remarks

In the past few weeks we have tried to estimate the runs a catcher may contribute by inducing the umpire to call more strikes. We progressively refined our estimation, but the final value has not moved by much. A catcher can contribute more than one win (probably even two wins) just by his ability to frame pitches.

As we mentioned, there is room for further improvement of the model. The ballpark should be part of the model, and pitches at the four corners are still out of the equation. Thus, we might still be on the conservative side when we state one or two wins as the ceiling.

Several of the top catchers in the proposed lists are backups. This makes sense, since it's so hard to find one good-hitting catcher, let alone two. So, when looking at replacements, teams hunt for some other assets, like defense and ability to handle pitchers. Teams have probably figured that out for a while, but this analysis show that if you pay attention, you can get one extra win just by selecting the right backup catcher.

So just how good is Yadier Molina on defense? FanGraphs credits him with five to eight fielding runs each year from 2005 to 2010. For the last three seasons, we can add another 19, nine and 16 runs on top of that due to his ability at framing pitches.

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Top games of the week: June 26-July 2

Max Marchi — 2011-07-03T10:48:15+00:00

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